scholarly journals A fast Bayesian model for latent radio signal prediction

2009 ◽  
Author(s):  
B. Houlding ◽  
A. Bhattacharya ◽  
S.P. Wilson ◽  
T.K. Forde
Keyword(s):  
Bayesian Model ◽  
Radio Signal ◽  
Signal Prediction

scholarly journals An Empirical Study on Using CNNs for Fast Radio Signal Prediction

2022 ◽  
Vol 3 (2) ◽  
Author(s):  
Ozan Ozyegen ◽  
Sanaz Mohammadjafari ◽  
Mucahit Cevik ◽  
Karim El mokhtari ◽  
Jonathan Ethier ◽  
...  
Keyword(s):  
Empirical Study ◽  
Radio Signal ◽  
Signal Prediction

A Novel Ray-Tracing Technique for Indoor Radio Signal Prediction

2011 ◽  
Vol 25 (8-9) ◽  
pp. 1179-1190 ◽  
Author(s):  
M. S. Sarker ◽  
A. W. Reza ◽  
K. Dimyati
Keyword(s):  
Ray Tracing ◽  
Radio Signal ◽  
Signal Prediction ◽  
Indoor Radio

Evaluating the Validity of a Bayesian Program for Predicting Stage in Hodgkin’s Disease

1981 ◽  
Vol 20 (03) ◽  
pp. 174-178 ◽  
Author(s):  
A. I. Barnett ◽  
J. Cynthia ◽  
F. Jane ◽  
Nancy Gutensohn ◽  
B. Davies
Keyword(s):  
New England ◽  
Hodgkin's Disease ◽  
Bayesian Model ◽  
Medical Center ◽  
Hodgkin’S Disease ◽  
Test Procedure ◽  
Disease Patient ◽  
Probabilistic Information ◽  
Probabilistic Forecasts ◽  
England Medical

A Bayesian model that provides probabilistic information about the spread of malignancy in a Hodgkin’s disease patient has been developed at the Tufts New England Medical Center. In assessing the model’s reliability, it seemed important to use it to make predictions about patients other than those relevant to its construction. The accuracy of these predictions could then be tested statistically. This paper describes such a test, based on 243 Hodgkin’s disease patients of known pathologic stage. The results obtained were supportive of the model, and the test procedure might interest those wishing to determine whether the imperfections that attend any attempt to make probabilistic forecasts have gravely damaged their accuracy.

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

AN INFORMATION ENTROPY CRITERION OF RADIO SIGNAL RECEPTION QUALITY

2011 ◽  
Vol 70 (5) ◽  
pp. 413-423
Author(s):  
E. V. Kravtsov ◽  
S. N. Panychev
Keyword(s):  
Information Entropy ◽  
Radio Signal ◽  
Entropy Criterion
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